Total energy expenditure (TEE) was measured simultaneously in 36 free-living children aged 7, 9, 12, and 15 y over 10-15 d by the doubly labeled water (DLW) method and for 2-3 separate days by heart-rate (HR) monitoring. The 95% confidence limits of agreement (mean difference +/- 2SD) were -1.99 to +1.44 MJ/d. HR TEE discrepancies ranged from -16.7% to +18.8% with 23 values lying within +/- 10% of DLW TEE estimates. Boys and girls spent 462 +/- 108 and 318 +/- 120 min/d, respectively, in total physical activity (P less than 0.01). Time spent in moderate and vigorous physical activity (MVPA) was 68 +/- 37 min/d by younger children (7-9 y) and 34 +/- 24 min/d by older children (12-15 y) (P less than 0.001). Younger boys engaged in MVPA (91 +/- 33 min/d) and vigorous physical activity (VPA) (35 +/- 15 min/d) significantly longer than younger girls (MVPA, 39 +/- 16 min/d, P less than 0.001; VPA, 10 +/- 4 min/d, P less than 0.01) as did older boys (MVPA, 52 +/- 21 min/d; VPA, 30 +/- 18 min/d) compared with older girls (MVPA, 15 +/- 10 min/d; VPA, 8 +/- 5 min/d). HR monitoring provides a close estimation of the TEE of population groups and objective assessment of associated patterns of physical activity.

Advances in machine learning have changed many fields of study and it has also drawn attention in a variety of remote sensing applications. In particular, deep convolutional neural networks (CNNs) have proven very useful in fields such as image recognition; however, the use of CNNs in large-scale remote sensing landcover classifications still needs further investigation. We set out to test CNN-based landcover classification against a more conventional XGBoost shallow learning algorithm for mapping a notoriously difficult group of landcover classes, wetland class as defined by the Canadian Wetland Classification System. We developed two wetland inventory style products for a large (397,958 km2) area in the Boreal Forest region of Alberta, Canada, using Sentinel-1, Sentinel-2, and ALOS DEM data acquired in Google Earth Engine. We then tested the accuracy of these two products against three validation data sets (two photo-interpreted and one field). The CNN-generated wetland product proved to be more accurate than the shallow learning XGBoost wetland product by 5%. The overall accuracy of the CNN product was 80.2% with a mean F1-score of 0.58. We believe that CNNs are better able to capture natural complexities within wetland classes, and thus may be very useful for complex landcover classifications. Overall, this CNN framework shows great promise for generating large-scale wetland inventory data and may prove useful for other landcover mapping applications.

We present new coarse resolution (0.5° &times; 0.5°) vegetation height and vegetation-cover fraction data sets between 60° S and 60° N for use in climate models and ecological models. The data sets are derived from 2003–2009 measurements collected by the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat), the only LiDAR instrument that provides close to global coverage. Initial vegetation height is calculated from GLAS data using a development of the model of Rosette et al. (2008) with with further calibration on desert sites. Filters are developed to identify and eliminate spurious observations in the GLAS data, e.g. data that are affected by clouds, atmosphere and terrain and as such result in erroneous estimates of vegetation height or vegetation cover. Filtered GLAS vegetation height estimates are aggregated in histograms from 0 to 70 m in 0.5 m intervals for each 0.5° &times; 0.5°. The GLAS vegetation height product is evaluated in four ways. Firstly, the Vegetation height data and data filters are evaluated using aircraft LiDAR measurements of the same for ten sites in the Americas, Europe, and Australia. Application of filters to the GLAS vegetation height estimates increases the correlation with aircraft data from <i>r</i> = 0.33 to <i>r</i> = 0.78, decreases the root-mean-square error by a factor 3 to about 6 m (RMSE) or 4.5 m (68% error distribution) and decreases the bias from 5.7 m to −1.3 m. Secondly, the global aggregated GLAS vegetation height product is tested for sensitivity towards the choice of data quality filters; areas with frequent cloud cover and areas with steep terrain are the most sensitive to the choice of thresholds for the filters. The changes in height estimates by applying different filters are, for the main part, smaller than the overall uncertainty of 4.5–6 m established from the site measurements. Thirdly, the GLAS global vegetation height product is compared with a global vegetation height product typically used in a climate model, a recent global tree height product, and a vegetation greenness product and is shown to produce realistic estimates of vegetation height. Finally, the GLAS bare soil cover fraction is compared globally with the MODIS bare soil fraction (<i>r</i> = 0.65) and with bare soil cover fraction estimates derived from AVHRR NDVI data (<i>r</i> = 0.67); the GLAS tree-cover fraction is compared with the MODIS tree-cover fraction (<i>r</i> = 0.79). The evaluation indicates that filters applied to the GLAS data are conservative and eliminate a large proportion of spurious data, while only in a minority of cases at the cost of removing reliable data as well. <br><br> The new GLAS vegetation height product appears more realistic than previous data sets used in climate models and ecological models and hence should significantly improve simulations that involve the land surface